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Itemized deductions Breakdown #38

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371 changes: 371 additions & 0 deletions us/deductions/itemized_deductions.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from policyengine_us import Microsimulation\n",
"from policyengine_core.reforms import Reform\n",
"\n",
"behavioral_responses = Reform.from_dict({\n",
" \"gov.simulation.labor_supply_responses.elasticities.income\": {\n",
" \"2024-01-01.2100-12-31\": -0.05\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.1\": {\n",
" \"2024-01-01.2100-12-31\": 0.31\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.2\": {\n",
" \"2024-01-01.2100-12-31\": 0.28\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.3\": {\n",
" \"2024-01-01.2100-12-31\": 0.27\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.4\": {\n",
" \"2024-01-01.2100-12-31\": 0.27\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.5\": {\n",
" \"2024-01-01.2100-12-31\": 0.25\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.6\": {\n",
" \"2024-01-01.2100-12-31\": 0.25\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.7\": {\n",
" \"2024-01-01.2100-12-31\": 0.22\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.8\": {\n",
" \"2024-01-01.2100-12-31\": 0.22\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.9\": {\n",
" \"2024-01-01.2100-12-31\": 0.22\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.secondary\": {\n",
" \"2024-01-01.2100-12-31\": 0.27\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.10\": {\n",
" \"2024-01-01.2100-12-31\": 0.22\n",
" }\n",
"}, country_id=\"us\")\n",
"\n",
"baseline = Microsimulation(dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")\n",
"\n",
"baseline_behavioral = Microsimulation(dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"repeal_it_ded_reform = Reform.from_dict(\n",
" {\n",
" \"gov.irs.deductions.deductions_if_itemizing\": {\n",
" \"2024-01-01.2100-12-31\": [\"wagering_losses_deduction\", \"qualified_business_income_deduction\"]\n",
" },\n",
" \"gov.irs.deductions.itemized_deductions\": {\n",
" \"2024-01-01.2100-12-31\": [\"heat_pump_expenditures\"]\n",
" }\n",
" }\n",
")\n",
"\n",
"repeal_medical_exp_reform = Reform.from_dict(\n",
" {\n",
" \"gov.irs.deductions.itemized.medical.floor\": {\n",
" \"2024-01-01.2100-12-31\": 1_000_000_000\n",
" }\n",
" }\n",
")\n",
"\n",
"repeal_chart_ded_reform = Reform.from_dict(\n",
" {\n",
" \"gov.irs.deductions.itemized.charity.ceiling.all\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" }\n",
" }\n",
")\n",
"\n",
"repeal_interest_ded_reform = Reform.from_dict(\n",
" {\n",
" \"gov.irs.deductions.deductions_if_itemizing\": {\n",
" \"2024-01-01.2100-12-31\": [\"medical_expense_deduction\", \"charitable_deduction\", \"salt_deduction\", \"casualty_loss_deduction\", \"qualified_business_income_deduction\", \"wagering_losses_deduction\"]\n",
" },\n",
" \"gov.irs.deductions.itemized_deductions\": {\n",
" \"2024-01-01.2100-12-31\": [\"medical_expense_deduction\", \"charitable_deduction\", \"salt_deduction\", \"casualty_loss_deduction\"]\n",
" }\n",
" }\n",
")\n",
"\n",
"repeal_salt_ded_reform = Reform.from_dict(\n",
" {\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.JOINT\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.SINGLE\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.SEPARATE\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.SURVIVING_SPOUSE\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.HEAD_OF_HOUSEHOLD\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" }\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Dictionary of all the reforms\n",
"reforms = {\n",
" \"repeal_it_ded\": repeal_it_ded_reform,\n",
" \"repeal_medical_exp\": repeal_medical_exp_reform,\n",
" \"repeal_chart_ded\": repeal_chart_ded_reform,\n",
" \"repeal_interest_ded\": repeal_interest_ded_reform,\n",
" \"repeal_salt_ded\": repeal_salt_ded_reform,\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"baseline_income = baseline.calculate(\"household_net_income\", period=2024)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reform: repeal_it_ded -> Impact on income tax: -87,298,748,961.58\n",
"Reform: repeal_medical_exp -> Impact on income tax: -4,548,085,250.17\n",
"Reform: repeal_chart_ded -> Impact on income tax: -67,633,722,372.78\n",
"Reform: repeal_interest_ded -> Impact on income tax: -28,691,421,174.73\n",
"Reform: repeal_salt_ded -> Impact on income tax: -10,118,681,939.88\n"
]
}
],
"source": [
"for name, reform in reforms.items():\n",
" sim = Microsimulation(reform=reform,\n",
" dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")\n",
" reformed_income = sim.calculate(\"household_net_income\", period=2024)\n",
" impact = (reformed_income - baseline_income).sum()\n",
" print(f\"Reform: {name} -> Impact on income tax: {impact:,.2f}\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "type object 'reform' has no attribute 'to_dict'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[11], line 8\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Loop over each reform, combine it with behavioral responses, and compute the impact\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, reform \u001b[38;5;129;01min\u001b[39;00m reforms\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# Merge the specific reform with the behavioral responses.\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Here we assume that each 'Reform' object exposes a to_dict() method to get its underlying dict.\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m combined_reform_dict \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[43mreform\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_dict\u001b[49m(), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mbehavioral_responses\u001b[38;5;241m.\u001b[39mto_dict()}\n\u001b[1;32m 9\u001b[0m combined_reform \u001b[38;5;241m=\u001b[39m Reform\u001b[38;5;241m.\u001b[39mfrom_dict(combined_reform_dict, country_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mus\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Initialize a new microsimulation with the combined reform\u001b[39;00m\n",
"\u001b[0;31mAttributeError\u001b[0m: type object 'reform' has no attribute 'to_dict'"
]
}
],
"source": [
"# First, re-calculate the baseline income tax in case you haven't already:\n",
"baseline_income = baseline.calculate(\"income_tax\", period=2024)\n",
"\n",
"# Loop over each reform, combine it with behavioral responses, and compute the impact\n",
"for name, reform in reforms.items():\n",
" # Merge the specific reform with the behavioral responses.\n",
" # Here we assume that each 'Reform' object exposes a to_dict() method to get its underlying dict.\n",
" combined_reform_dict = {**reform.to_dict(), **behavioral_responses.to_dict()}\n",
" combined_reform = Reform.from_dict(combined_reform_dict, country_id=\"us\")\n",
" \n",
" # Initialize a new microsimulation with the combined reform\n",
" sim = Microsimulation(\n",
" reform=combined_reform,\n",
" dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\"\n",
" )\n",
" \n",
" # Calculate the income tax for the combined reform scenario\n",
" reformed_income = sim.calculate(\"income_tax\", period=2024)\n",
" \n",
" # Compute the impact. Here we use the same arithmetic as before.\n",
" impact = (reformed_income - baseline_income).sum()\n",
" print(f\"Combined Reform: {name} with behavioral responses -> Impact on income tax: {impact:,.2f}\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Combined Reform: repeal_it_ded with behavioral responses -> Impact on income tax: 80,194,493,082.39\n",
"Combined Reform: repeal_medical_exp with behavioral responses -> Impact on income tax: 3,313,528,155.87\n",
"Combined Reform: repeal_chart_ded with behavioral responses -> Impact on income tax: 62,512,730,450.91\n",
"Combined Reform: repeal_interest_ded with behavioral responses -> Impact on income tax: 25,320,345,309.85\n",
"Combined Reform: repeal_salt_ded with behavioral responses -> Impact on income tax: 8,884,419,415.79\n"
]
}
],
"source": [
"# Calculate the baseline income tax (if not already done)\n",
"baseline_income = baseline.calculate(\"income_tax\", period=2024)\n",
"\n",
"# Define the original individual reform dictionaries\n",
"reforms_data = {\n",
" \"repeal_it_ded\": {\n",
" \"gov.irs.deductions.deductions_if_itemizing\": {\n",
" \"2024-01-01.2100-12-31\": [\"wagering_losses_deduction\", \"qualified_business_income_deduction\"]\n",
" },\n",
" \"gov.irs.deductions.itemized_deductions\": {\n",
" \"2024-01-01.2100-12-31\": [\"heat_pump_expenditures\"]\n",
" }\n",
" },\n",
" \"repeal_medical_exp\": {\n",
" \"gov.irs.deductions.itemized.medical.floor\": {\n",
" \"2024-01-01.2100-12-31\": 1_000_000_000\n",
" }\n",
" },\n",
" \"repeal_chart_ded\": {\n",
" \"gov.irs.deductions.itemized.charity.ceiling.all\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" }\n",
" },\n",
" \"repeal_interest_ded\": {\n",
" \"gov.irs.deductions.deductions_if_itemizing\": {\n",
" \"2024-01-01.2100-12-31\": [\n",
" \"medical_expense_deduction\", \"charitable_deduction\", \"salt_deduction\",\n",
" \"casualty_loss_deduction\", \"qualified_business_income_deduction\", \"wagering_losses_deduction\"\n",
" ]\n",
" },\n",
" \"gov.irs.deductions.itemized_deductions\": {\n",
" \"2024-01-01.2100-12-31\": [\n",
" \"medical_expense_deduction\", \"charitable_deduction\", \"salt_deduction\", \"casualty_loss_deduction\"\n",
" ]\n",
" }\n",
" },\n",
" \"repeal_salt_ded\": {\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.JOINT\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.SINGLE\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.SEPARATE\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.SURVIVING_SPOUSE\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" },\n",
" \"gov.irs.deductions.itemized.salt_and_real_estate.cap.HEAD_OF_HOUSEHOLD\": {\n",
" \"2024-01-01.2100-12-31\": 0\n",
" }\n",
" }\n",
"}\n",
"\n",
"# Define behavioral responses as a dictionary\n",
"behavioral_responses_data = {\n",
" \"gov.simulation.labor_supply_responses.elasticities.income\": {\n",
" \"2024-01-01.2100-12-31\": -0.05\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.1\": {\n",
" \"2024-01-01.2100-12-31\": 0.31\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.2\": {\n",
" \"2024-01-01.2100-12-31\": 0.28\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.3\": {\n",
" \"2024-01-01.2100-12-31\": 0.27\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.4\": {\n",
" \"2024-01-01.2100-12-31\": 0.27\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.5\": {\n",
" \"2024-01-01.2100-12-31\": 0.25\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.6\": {\n",
" \"2024-01-01.2100-12-31\": 0.25\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.7\": {\n",
" \"2024-01-01.2100-12-31\": 0.22\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.8\": {\n",
" \"2024-01-01.2100-12-31\": 0.22\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.9\": {\n",
" \"2024-01-01.2100-12-31\": 0.22\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.primary.10\": {\n",
" \"2024-01-01.2100-12-31\": 0.22\n",
" },\n",
" \"gov.simulation.labor_supply_responses.elasticities.substitution.by_position_and_decile.secondary\": {\n",
" \"2024-01-01.2100-12-31\": 0.27\n",
" }\n",
"}\n",
"\n",
"# Loop over each individual reform, merge it with behavioral responses, and compare to baseline\n",
"for name, reform_dict in reforms_data.items():\n",
" # Combine the individual reform with behavioral responses.\n",
" combined_dict = {**reform_dict, **behavioral_responses_data}\n",
" \n",
" # Create a combined Reform from the merged dictionary\n",
" combined_reform = Reform.from_dict(combined_dict, country_id=\"us\")\n",
" \n",
" # Initialize the microsimulation using the combined reform\n",
" sim = Microsimulation(\n",
" reform=combined_reform,\n",
" dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\"\n",
" )\n",
" \n",
" # Calculate the income tax for the combined scenario\n",
" reformed_income_tax = sim.calculate(\"income_tax\", period=2024)\n",
" \n",
" # Compute the impact relative to the baseline (using the same arithmetic as before)\n",
" impact = (reformed_income_tax - baseline_income).sum()\n",
" print(f\"Combined Reform: {name} with behavioral responses -> Impact on income tax: {impact:,.2f}\")"
]
}
],
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"kernelspec": {
"display_name": "pe",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
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